scholarly journals Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification

2013 ◽  
Vol 18 (7) ◽  
pp. 076002 ◽  
Author(s):  
Ludguier D. Montejo ◽  
Jingfei Jia ◽  
Hyun K. Kim ◽  
Uwe J. Netz ◽  
Sabine Blaschke ◽  
...  
2013 ◽  
Vol 18 (7) ◽  
pp. 076001 ◽  
Author(s):  
Ludguier D. Montejo ◽  
Jingfei Jia ◽  
Hyun K. Kim ◽  
Uwe J. Netz ◽  
Sabine Blaschke ◽  
...  

2021 ◽  
Vol 27 (3) ◽  
pp. 747-759
Author(s):  
Charles Arputham ◽  
Krishnaraj Nagappan ◽  
Lenin Babu Russeliah ◽  
AdalineSuji Russeliah

Author(s):  
Kalyani S. Boral ◽  
V. T. Gaikwad

Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages of diseases. Medical informatics is the study that combines two medical data sources: biomedical record and imaging data. Medical image data is formed by pixels that correspond to a part of a physical object and produced by imaging modalities. discovery of medical image data methods is a challenge in the sense of getting their insight value, analyzing and diagnosing of a specific disease. Image classification plays an important role in computer-aided-diagnosis of diseases and is a big challenge on image analysis tasks. This challenge related to the usage of methods and techniques in exploiting image processing result, pattern recognition result and classification methods and subsequently validating the image classification result into medical expert knowledge. The main objective of medical images classification is to reach high accuracy to identify the name of disease. It showed the improvement of image classification techniques such as to increase accuracy and sensitivity value and to be feasible employed for computer-aided-diagnosis are a big challenge and an open research.


In this paper image mining concepts have been used for the diagnosis of the infected cells from the medical images. It manages the certain information extraction, picture information relationship and different examples which are not unequivocally put away in the pictures. This procedure is an expansion of information mining to picture area. Though the medical images are diagnosed using CT-scan and CAD (computer aided diagnosis) nearly 10-30% of the affected cells are not predicted but using this technique the medical images can be clearly diagnosed.


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